Land Surface Temperature Inversion Research Articles

Study on temperature regulation function of green spaces at community scale in high-density urban areas and planning design strategies

In recent years, the frequency of extreme high-temperature events has gradually increased. To better understand the impact of urban green space coverage's spatial pattern on land surface temperature (LST), this study selected three sample areas along the urban-rural gradient in Shanghai. Using LST inversion and resampling methods, LST data for different grid sizes were obtained for spring, summer, and winter in the 2010s, 2015s, and 2020 s. A boosting regression tree model was employed to determine the key indicators affecting LST and effective cooling thresholds. The impact of green quality, structure, and pattern at the community scale on LST was discussed, providing feasible suggestions for green space planning and design in different urban spaces. The study found significant differences in the spatial patterns of green areas and LST among the three sample regions. The quantity and integrity of green spaces in the Huangpu, Minhang, and Songjiang sample areas have progressively improved, with overall green coverages of 19 %, 38 %, and 43 %, respectively. As grid size increases, the relative influence of fractional vegetation coverage (FVC) on LST generally decreases, whereas the relative influence of green structure and patterns gradually increases. Taking the summer of the 2020 s as an example, the influence of FVC on the three study sample areas was 80.79, 83.36, and 87.18 at a 30 m grid size, which decreased to 48.87, 40.59, and 47.64 at a 120 m grid size. The green structure's impact rose from 13.09, 15.22, and 10.6–30.01, 51.82, and 38.5; the influence of green patterns increased from 6.12, 1.42, and 2.21–21.13, 7.6, and 13.86. Key indicators affecting LST include FVC (Fractional Vegetation Coverage), AREA (Green Patch Area), PD (Patch Density), COHESION (Patch Cohesion Index), and ED (Edge Density). High temperatures in summer are one of the ecological issues that need special attention in Shanghai's green space design. Setting the green space proportion to 35 % while avoiding fragmentation and low vegetation coverage can achieve effective cooling. This study's main advancement lies in utilizing machine learning algorithms to identify the principal green spatial pattern impact factors and key thresholds influencing LST at the community scale in Shanghai. The related results and proposed strategies provide a research framework and strong basis for special regulations in urban green space system planning concerning urban thermal environments. They offer references for urban renewal and new town planning to address climate change and urban high-temperature issues, such as clear requirements for optimal green space area at the community scale, community park spatial layout and quantity regulations, and urban park planning design suggestions. This study also highlights obstacles in the practical application of planning and design strategies, which can help avoid difficulties in implementing planning policies. It attempts to set goals for community-scale vegetation planning in China from a policy perspective and provides relevant recommendations.

Estimation of Land Surface Temperature from Chinese ZY1-02E IRS Data

The role of land surface temperature (LST) is of the utmost importance in multiple academic disciplines, such as climatology, hydrology, ecology, and meteorology. To date, many methods have been proposed to estimate LST from satellite thermal infrared data. The single-channel (SC) algorithm can provide an accurate result in retrieving LST based on prior knowledge of known land surface emissivity (LSE). The SC algorithm is extensively employed for retrieving LST from Landsat series data due to its simplicity and its reliance on just one thermal infrared channel. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12 μm, which is affected strongly by both atmospheric water vapor and ozone. Three study areas, namely the Baotou, Heihe River Basin, and Yantai Sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted via cross-comparison between the retrieved LST and MODIS LST products. The results revealed that the WVS-based method exhibited an average bias of 0.63 K and an RMSE of 1.62 K compared to the in situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77 K and an RMSE of 2.0 K. These findings indicate that the WVS-based method is effective in estimating LST from ZY1-02E IRS data.

A Remote Sensing Analysis of the Urban Heat Island Effect in Guangzhou City from 2018 to 2023

This study aims to assess the urban heat island (UHI) effect in Guangzhou City using remote sensing technology and to explore its potential impact on urban spatial layout and measures for mitigating the UHI effect. By analyzing Landsat 8 satellite data, this paper focuses on the changes in land surface temperature in Guangzhou from 2018 to 2023, using a weekly scale as the primary time analysis unit. The research method is based on the Radiative Transfer Equation (RTE) for the inversion of land surface temperature, utilizing ArcMap software for the related data calculations and processing. The research findings reveal the weekly scale distribution of land surface temperature in Guangzhou City from 2018 to 2023, showing that the UHI effect is mainly concentrated in the southwestern part of the city. In contrast, due to less economic activity, lower population density, and a larger area of green coverage, the northeastern part experiences a relatively weaker UHI effect. The discoveries of this paper provide important references and bases for spatial planning and mitigation of the UHI effect in Guangzhou City and other similar cities, emphasizing the application value of remote sensing technology in the study of urban climate change.

INFLUENCE FACTORS OF LAND SURFACE TEMPERATURE INVERSION USING THERMAL INFRARED HYPERSPECTRAL REMOTE SENSING SATELLITES DATA

Abstract. Land surface temperature (LST) is widely used in research fields such as numerical forecasting, global circulation models, and regional climate models. For the remote sensing data from satellites with thermal infrared detection capability, the land surface temperature (LST), land surface emissivity (LSE), and atmospheric influence are mixed together. Using different assumptions and approximations for the radiative transfer equations and surface emissivity, various LST algorithms have been proposed. Among these algorithms, the split-window (SW) algorithm is currently most widely used. Besides, with the rapid development of machine learning, new ideas have been emerged for quantitative remote sensing inversion. For a hyperspectral remote sensing satellite with over 20 thermal infrared channels, machine learning methods such as random forest and artificial neural network can be selected to build an integrated separation and inversion algorithm for LST and LSE.In this paper, the influencing factors of LST inversion using thermal infrared hyperspectral satellites data is discussed, taking the SW algorithm and integrated machine learning algorithm as examples, and the contribution of these factors to the LST inversion error is analysed. We hope this paper could provide valuable reference for the design, index analysis and error calculation for remote sensing satellites with thermal infrared hyperspectral detection capability.

Satellite-Based Optimization and Planning of Urban Ventilation Corridors for a Healthy Microclimate Environment

Urban ventilation corridors (UVCs) have the potential to effectively mitigate urban heat islands and air pollution. Shanghai, a densely populated city located in eastern China, is among the hottest cities in the country and requires urgent measures in order to enhance its ventilation system. This study introduces a novel approach that integrates land surface temperature retrieval, PM2.5 concentration retrieval, and wind field simulation to design UVCs at the city level. Through remote sensing data inversion of land surface temperature (LST) and PM2.5 concentration, the study identifies the action spaces and compensation spaces for UVCs. The Weather Research and Forecasting (WRF) model, coupled with the multilayer urban scheme Building Effect Parameterization (BEP) model, is employed to numerically simulate and analyze the wind field. Based on the identification of thirty high-temperature zones and high PM2.5 concentration zones as action spaces, and twenty-two low-temperature zones and low PM2.5 concentration zones as compensation spaces in Shanghai, the study constructs seven first-class ventilation corridors and nine secondary ventilation corridors according to local circulation patterns. Unlike previous UVC research, this study assesses the cleanliness of cold air, which is a common oversight in UVC planning. Ignoring the assessment of cold air cleanliness can result in less effective UVCs in improving urban air quality and even exacerbate air pollution in the central city. Therefore, this study serves as a crucial contribution by rectifying this significant deficiency. It not only provides a fresh perspective and methodology for urban-scale ventilation corridor planning but also contributes to enhancing the urban microclimate by mitigating the effects of urban heat islands and reducing air pollution, ultimately creating a livable and comfortable environment for urban residents.

New framework for land surface temperature and emissivity inversion based on hyperspectral thermal infrared data

ABSTRACT Hyperspectral thermal infrared (HTIR) data offers enormous potential for land surface temperature (LST) and emissivity (LSE) inversion. Methods to fully use a large amount of spectral information, while avoiding data redundancy, is constantly being explored. Therefore, a new framework for LST and LSE inversion using HTIR data is proposed in this study. First, the stepwise iteration method based on information content was used to select channels that were more sensitive to land surface information. Principal component analysis (PCA) and a two-step machine learning method were then used to retrieve the LST and LSE based on the selected channels. The findings demonstrate that the LST and spectra of the LSE can be obtained simultaneously based on the proposed framework. An LST inversion accuracy of 1.5K and an emissivity inversion accuracy of 0.017 can be achieved. However, an increase in the number of channels does not result in superior outcomes. Satisfactory inversion results can be obtained by selecting only 10–35 channels with the highest information content. In conclusion, this framework can be used to guide channel selection and land surface parameters inversion for HTIR sensors.

Remote sensing inversion of land surface temperature for cloud coverage areas based on NDVI in the North China Plain

ABSTRACT As the cloud covers the surface thermal radiation during the transmission process and the remote sensor in the air is difficult to detect, Thermal Infrared (TIR) remote sensing inversion of Land Surface Temperature (LST) must be performed under clear-sky and cloudless conditions. This study has established the functional relationship between LST and the vegetation index of cloudless vegetation pixels around the cloud coverage areas in the North China Plain based on the Normalized Difference Vegetation Index (NDVI) to address the issues of TIR remote sensing inversion for LST affected by cloud coverage. By acquiring NDVI and using short-term, relatively stable characteristics of vegetation, the LST of the cloud cover area is estimated. The findings show a linear negative correlation between LST and NDVI in vegetation pixels, with the vegetation type remaining essentially unchanged over time. When there are 20 pixels in each of the two thermal infrared channels of FY-3 D, the MAE value and RMSE value of the 24th thermal infrared channel are 0.77 and 0.88, respectively, and the MAE value and RMSE value of the 25th thermal infrared channel are 0.64 and 0.80, respectively. When the number of pixels is 200, the 24th thermal infrared channel’s MAE value and RMSE values are 0.96 and 0.99, respectively, while the 25th thermal infrared channel’s MAE value and RMSE values are 0.90 and 0.95, respectively. In other words, the estimation is more accurate and closer to the true value, and the land surface temperature retrieved by the 25th channel deviates from the true value to a lesser extent. The average absolute error and root mean square error are both less than 1, which may satisfy the accuracy demands of practical applications such as agricultural drought monitoring, ecological evaluation, and crop yield estimation.

Impact of 3-D structures on directional effective emissivity in urban areas based on DART model

Thermal infrared (TIR) remote sensing is effective to monitor urban land surface temperature (LST) on a large scale. The accuracy of the inversion of LST depends strongly on the emissivity. The emissivity is complex in urban areas and is largely dependent on three-dimensional (3-D) urban structure and different building materials, also called effective emissivity. The urban canyons have homogeneous temperatures to avoid the difficulty of defining the emissivity of non-isothermal surfaces. In this paper, we use the 3-D discrete anisotropic radiative transfer (DART) model to simulate the emission and propagation of thermal radiation in urban scenes by a flux tracking method and generate remote sensing images. The impact of 3-D structures on the effective emissivity was analyzed and simple relations between effective emissivity and 3-D structure parameters were proposed. Results show that the increase of emissivity at nadir is well related to wall frontal area index (F) and the mean sky view factor of flat surfaces (i.e., roof and road, SVFweighted), whereas emissivity directionality is well related to road's sky view factor (SVFroad). Overall, emissivity directionality (reaches 0.069 if reflectance is 0.2 for the road and 0.1 for the other components) due to material mixture and nadir emissivity increment (increase by 0.0646 for material reflectance R of 0.1) caused by 3-D structures can be larger than other emissivity sources. This study helps to understand the impact of 3-D structures and different material emissivity on urban effective emissivity and offers insights for addressing urban emissivity directionality in multi-angular remote sensing missions.

Detecting geothermal anomalies using Landsat 8 thermal infrared remote sensing data in the Ruili Basin, Southwest China.

With the recent increase in global focus on green energy, the application of thermal infrared remote sensing data for the detection of geothermal anomalies has attracted wide attention as it can overcome the difficulty of using only ground surveying. This study aimed to highlight areas of geothermal anomalies with land surface temperature (LST) time series data in winter derived from thermal infrared remote sensing. To extract LST anomaly areas in the Ruili Basin for geothermal prospecting, nine types of data on the study area in winter during 2014 ~ 2021 from Landsat 8 were analyzed. Landsat 8 LST inversion data based on the mono-window algorithm (MWA) can be used to identify hot springs, volcanoes, and other heat-related phenomena. Superimposing LST anomalies for each cycle through drilling data, excluding the heat island effect, geothermal anomaly regions could be plotted. The results show that the accuracy of MWA LST varied within 2K, which is acceptable for geothermal energy and higher than those of the radiative transfer equation (RTE) algorithm and MODIS LST products. Three high-LST regions in the southeast of the study area were identified as geothermal anomaly areas (A, B, and C), and region B was further verified through a comprehensive field investigation of geothermal wells, supplemented by the temperature gradient (TG) method. The findings reveal that the distribution of geothermal anomaly areas and high-LST areas are highly consistent with the northeast trending fault structure; faults act as thermal channels and help in accurately detecting local LST anomalies. Overall, the infrared remote sensing method proved to be a valid technique for detecting LST anomalies. Considering the synergy between thermal infrared surface detection and subsurface exploration methods, the identification of known geothermal fields (B) and other possible areas (A and C) has significance in the upscaling of local geologic information to regional prospecting, thus providing a direction for future geothermal research.

Spatial and Temporal Inversion of Land Surface Temperature along Coastal Cities in Arid Regions

Climate change is undoubtedly affecting the global weather of the Earth. Rapid human civilization has mainly caused this in the last few decades. This research examined the spatial and temporal land surface temperature (LST) in the United Arab Emirates (UAE) coastal cities located in an arid region that faced massive urbanization over the last 50 years. We estimated the LST using by-products of Landsat and MODIS images covering 2000 until 2020. The assessment of LST was performed in two contexts, i.e., spatially and temporally, covering daytime vs. nighttime during the summer and winter seasons. Additionally, a supervised classification technique was adopted to extract the land use and land cover in the study area from the late 1970s until 2018. Unexpectedly, the results indicated that daytime LST in districts near the coastlines (heavily urbanized areas) are lower than the ones far away from the coast (about 9 °C). This observation represents the spatial LST inversion in the study area. Nevertheless, this difference was not observed during nighttime LST temporally, the daytime LST did not increase significantly during either summer or winter seasons. However, the nighttime LST has increased temporally by about 17% since 2000 (the temporal LST inversion). Both LST inversions could be attributed to the uniqueness of the study area, given that bare land desert was replaced by vegetation, high-rise buildings, and industrial activities. Additionally, the wind breeze blowing from the gulf might potentially contribute to cooling the coastal urban area during the daytime. Furthermore, in-depth zonal statistics were conducted to visualize the effect of land use on LST. The study observed that fully built-up areas with vegetation have lower LST than built-up areas without vegetation or a combination of sand and vegetation. The research outcomes are invaluable for decision-makers and researchers in achieving sustainable urban development.

Study on the spatial heterogeneity of urban heat islands and influencing factors

Conflicts between urban expansion and urban heat islands (UHIs) are increasing markedly, because of accelerated urbanization, and it is urgent to mitigate the UHI effect. In this study, we explored the spatial heterogeneity of UHIs from a neighborhood perspective via inversion of land surface temperature (LST) based on Landsat-8 remote sensing image data and using local Moran's I; the central urban area of Jinan City was selected as the study area. Furthermore, by constructing a system of urban spatial structure indicators and adopting methods such as correlation coefficient analysis and spatial econometric modeling, the factors influencing UHI were further revealed. The results showed that (1) There was spatial heterogeneity of the UHI in the central urban area of Jinan. The High-High cluster was mainly distributed in the north of the central urban area of Jinan; the Low-Low cluster was primarily distributed in the south of the central urban area of Jinan and the waters. Agglomeration distribution approximately coincided with heat island distribution. (2) The correlations between urban spatial structure indicators and LST in different dimensions were generally high and varied significantly. The two-dimensional and three-dimensional indicators all showed a highly significant positive relationship with LST. The mean and standard deviation of the DEM and absolute building height in the one-dimensional indicators were both negatively and significantly correlated with LST. (3) Building height standard deviation had a significantly positive spillover effect on the LST of the neighboring areas; this spillover effect was greater than the direct effect on these regions.

Accuracy Evaluation and Parameter Analysis of Land Surface Temperature Inversion Algorithm for Landsat-8 Data

Many researchers have developed a variety of land surface temperature (LST) inversion algorithms based on satellite data. The main LST inversion algorithms include Radiative Transfer Equation (RTE), Single Channel (SC) algorithm, Mono Window (MW) algorithm, and Split Window (SW) algorithm. In this study, nine LST inversion algorithms were designed using Landsat-8 data and meteorological station data to test the inversion efficiency of different algorithms in different seasons and different locations. The results show that the error of various LST inversion algorithms will increase with the rise of LST. R2 of the inversion results of each LST algorithm and the measured data are all greater than 0.73°C in winter and about 0.5°C in the other seasons. By analyzing the stability of various algorithms inside and outside the city, it is found that the stability of each LST inversion algorithm inside the city is better than that outside the city. For the same surface features, the inversion temperature inside the city is 3–5°C higher than that outside the city. In addition, the sensitivity of various inversion algorithms to parameters was also analyzed. The influence of atmospheric transmittance on RTE, SC, and MW inversion algorithms is in logarithmic form. The effect of emissivity on each algorithm is linear. The influence of NDVI on the algorithms is mainly through the estimation of surface emissivity parameters to affect the inversion results. The effect of ascending radiation on SC (LST4 and LST5) is linear and on RTE (LST1 and LST2) is logarithmic. The effect of downslope radiation on SC and RTE is linear. The influence of atmospheric water vapor content on SW (LST7) is nonlinear.

Noise-sensitivity Analysis and Improvement of Automatic Retrieval of Temperature and Emissivity Using Spectral Smoothness

There are numerous algorithms that can be used to retrieve land surface temperature (LST) and land surface emissivity (LSE) from hyperspectral thermal infrared (HTIR) data. The algorithms are sensitive to a number of factors, where noise is difficult to handle due to its unpredictability. Although there is a lot of research regarding the influence of noise on retrieval errors, few studies have focused on the mechanism. In this study, we selected the automatic retrieval of temperature and emissivity using spectral smoothness (ARTEMISS) algorithm—the representative of the iterative spectral smoothness temperature-emissivity separation algorithm family—as the research object and proposed an improved algorithm. First, we analyzed the influence mechanism of noise on the retrieval errors of ARTEMISS in theory. Second, we carried out a simulation and inversion experiment and analyzed the relationship between instrument spectral resolution, noise level, the ARTEMISS parameter setting and the retrieval errors separately. Last, we proposed an improved method (resolution-degrade-based spectral smoothness algorithm, RDSS) based on the mechanism and law of the influence of noise on retrieval errors and provided corresponding suggestions on instrument design. The results show that RDSS improves the accuracy of temperature inversion and is more effective for thermal infrared data with a high noise level and high spectral resolution, which can reduce the LST inversion error by up to 0.75 K and the LSE median absolute deviation (MAD) by 31%. In the presence of noise in HTIR data, the RDSS algorithm performs better than the ARTEMISS algorithm in terms of temperature-emissivity separation.

Calculation of land surface emissivity and retrieval of land surface temperature based on a spectral mixing model

Land surface emissivity (LSE) is a key parameter for the retrieval of land surface temperature (LST). Using the linear spectral mixing model (LSMM) to calculate the LSE can estimate the pixel composition at the sub-pixel level, which effectively solves the problem of mixed pixels when using the Landsat thermal infrared band to calculate the surface specific emissivity. In this paper, Landsat OLI/TIRS was used as the data source, surface emissivity was calculated using the LSMM of mixed pixels, and the LST was then obtained using the radiative transfer equation algorithm. At the same time, three threshold methods of the normalized difference vegetation index (NDVI) were used to calculate surface emissivity and then retrieve the LST. By comparing and analysing the inversion results and verifying the accuracy of the measured ground data, the surface emissivity and LST results obtained by the four methods were similar overall. Specifically, the maximum and average values of LST obtained by the LSMM were the highest, while those obtained by the Sobrino threshold method were the lowest, with an average difference of 0.63 °C. The difference between the LST inversion results of the LSMM and the other three methods were calculated separately, and the maximum change in LST reached 2.82 °C. All three ΔLST results showed high values in densely urbanised areas and low values in vegetation-covered areas. For urban areas with complex structures, the LSMM can be used to estimate the abundance of each component in a pixel at the sub-pixel scale, which can significantly improve the calculation accuracy of surface emissivity and lead to better LST inversion results.

Assessment of single-channel algorithms for land surface temperature retrieval at two southern Brazil sites

Land surface temperature (LST) is an essential parameter in investigating environmental, ecological processes and climate change, and thermal infrared remote sensing is a useful tool to acquire information regarding LST. Several accurate LST retrieval methodologies have been developed or refined in recent years and have demonstrated great potential. An assessment of various recent LST inversion single-channel (SC) algorithms is presented. These algorithms include improved mono-window, SC, and improved single channel (ISC). We compared the methods using two Brazilian sites, in which two kinds of validation were performed: field measurements with the satellite overpass and a comparative analysis using the web-based Atmospheric Correction Parameter Calculator tool and the radiative transfer equation (RTE) (assumed as reference). The three methods showed high coefficient of determination with the RTE (between 0.9 and 0.98). SC algorithm produced the furthest results from the reference and was statistically different. ISC algorithm provided the most reliable LST estimates, yielding root mean square errors between 1.53 and 1.91 K. LST can be retrieved through ISC algorithm only using meteorological station data, thus being an alternative for regions where radiosonde points have low density. Our findings contribute to more operational LST products from the Landsat series in humid places.

The relationships between urban-rural temperature difference and vegetation in eight cities of the Great Plains

Interpreting the relationship between urban heat island (UHI) and urban vegetation is a basis for understanding the impacts of underlying surfaces on UHI. The calculation of UHI intensity (UHII) requires observations from paired stations in both urban and rural areas. Due to the limited number of paired meteorological stations, many studies have used remotely sensed land surface temperature, but these time-series land surface temperature data are often heavily affected by cloud cover and other factors. These factors, together with the algorithm for inversion of land surface temperature, lead to accuracy problems in detecting the UHII, especially in cities with weak UHII. Based on meteorological observations from the Oklahoma Mesonet, a world-class network, we quantified the UHII and trends in eight cities of the Great Plains, USA, where data from at least one pair of urban and rural meteorological stations were available. We examined the changes and variability in urban temperature, UHII, vegetation condition (as measured by enhanced vegetation index, EVI), and evapotranspiration (ET). We found that both UHI and urban cold islands (UCI) occurred among the eight cities during 2000–2014 (as measured by impervious surface area). Unlike what is generally considered, UHII in only three cities significantly decreased as EVI and ET increased (p<0.1), indicating that the UHI or UCI cannot be completely explained simply from the perspective of the underlying surface. Increased vegetative cover (signaled by EVI) can increase ET, and thereby effectively mitigate the UHI. Each study station clearly showed that the underlying surface or vegetation affects urban-rural temperature, and that these factors should be considered during analysis of the UHI effect over time.

Split-Window Algorithm for Retrieval of Land Surface Temperature Using Landsat 8 Thermal Infrared Data

Land surface temperature (LST) plays a vital role in global climate change, radiation budgets, heat balance, vegetation, snowmelt, glacier hydrology, and geo-biophysical processes. It is, therefore, essential to determine LST precisely over large areas. With advancements in remote sensing, LST can now be estimated. In this study, a critical appraisal of various LST inversion algorithms is presented. These algorithms include mono-window (MW), split-window (SW), dual-angle (DA), single-channel (SC), and Sabmao method. The main objective is to derive an SW algorithm to retrieve LST from Landsat 8 satellite data and demonstrate the application to the Beas River basin in India. Located within the Himalayan range, the study area is characterized by heterogeneity and rugged terrain with areas covered by snow and glacier. The satellite imagery is a product of the Optical Land Imager (OLI) with a spatial resolution of 30 m and a thermal infrared sensor (TIRS) having a spatial resolution of 100 m. The SW algorithm requires spectral radiance and emissivity from two bands of the TIRS as input for the estimation of LST. The spectral radiance has been estimated using the TIRS bands 10 and 11. The normalized difference vegetation index (NDVI) and the threshold technique of OLI bands (2 to 5) have been used to derive the emissivity. The estimates of LST from the TIRS and OLI bands using the SW algorithm are found to be accurate and close to the in situ air temperature measurements and the LST values obtained from the MW algorithm. Results obtained show that the values of LST are high in the barren/rocky areas and low in the snow/glacier areas. The study reveals that the LST estimates from SW and MW algorithms are linearly transferable with negligible loss of accuracy. The LST estimates from the SW algorithm differs at most by up to 5 °C with the measured air temperature.

On the relationship between MODIS Land Surface Temperature and topography in Iran

Land Surface Temperature (LST) is considered important in monitoring the energy flux between the land surface and atmosphere. Due to the diversity of topography in Iran and its effect on the climate diversity, we decided to study the effect of topography on the LST variations. To this end, the LST digital data derived from the observations of the MODIS Terra and Aqua were used. The results indicated that, during the daytime, from sea level up to a height of 400 meters, the LST increased, and then the temperature decreased with increasing altitude, and up to a height of 3000 meters, there was a strong correlation between the two. LST lapse rate was more during the daytime compared with that of the night time and it was more during the winter compared with the summer. LST lapse rate showed larger variability in diurnal cycle, but its monthly patterns were similar in different aspects. The aspect had substantial effect on LST inversion elevation. Furthermore, the inverse relationship between LST and slope was strong in slopes up to 20°.

Analysis of Urban Change in Shenzhen City Based on Landsat Archived Data

With the ongoing development of economy and urbanization in China, the change of land use types has attracted more and more attention. In this study we focused on the urban development of Shenzhen City, Guangdong Province, analyzing Landsat 5 TM and Landsat 8 OLI data. We used an SVM based classification, a land transfer matrix approach, a directional growth analysis method and we calculated the inversion of land surface temperature to derive information of land cover changes that occurred in the time period between 1987 and 2017. The results are combined with Shenzhen’s economy, transportation policy and other aspects to find the driving forces of the urban development. The results show that during the observed 30 years, the area of construction land has increased significantly. Most of it is converted from other lands, and some of them are reclaimed. Most rapidly developing are areas west and northwest of the Bao’an, Nanshan and Longhua. The vegetated areas decreased slightly. Caused by the continuous increase of the construction land, the so-called heat island effect emerges slightly but continuously.

Land Surface Temperature Retrieval From FY-3C/VIRR Data and Its Cross-Validation With Terra/MODIS

Accurate inversion of land surface temperature (LST) from remote sensing data is an essential and challenging topic for earth observation applications. This paper successfully retrieves the LST from FY-3C/VIRR data with split-window method. With the simulated data, the algorithm coefficients are acquired with root mean square errors lower than 1.0 K for all subranges when view zenith angle (VZA) < 30° and the water vapor content (WVC) < 4.25 g/cm2 , as well as those in which the VZA < 30° and the LST < 307.5 K. In addition, a detailed sensitivity analysis is carried out. The analysis result indicates that the total LST uncertainty caused by the standard error of the algorithm, the uncertainties of land surface emissivity and WVC, and the instrument noise would be 1.22 K and 0.94 K for dry and wet atmosphere, respectively. Furthermore, LST retrieval method is applied to the visible and infrared radiometer measurements over the study area covering the geographical latitude of 31.671°N to 44.211°N and longitude of 10.739°W to 1.898°E, and the derived LST is cross-validated with Terra/MODIS LST product. The preliminary validation result shows that the split-window method determines the LST within 2.0 K for vegetation and soil areas.